Crimeless

The first part of our project is our Static Analysis. This part of our project consists of an exploratory study of crime in Boston and an analysis of which static factors (i.e. factors that do not change on an hourly or daily basis) are most likely to lead to a more crime-prone area.

The second part of our project is our Dynamic Analysis. This part of the project consists of predicting when and where a crime will occur by using dynamic factors (variables that change frequently over time such as weather, time of day, economic indicators, and school closings) to determine if a crime will occur or not in a given location at a given time. We then use the accuracy of our predictions and a probability of success at stopping a predicted crime to estimate an estimated positive economic impact on Boston.

Our project attempts to predict whether or not a crime will occur in a given location at a given time based on a set of dynamic factors in conjunction with static factors that we processed earlier. Ultimately, this type of data could be incredibly helpful for police in order to dynamically allocate their patrols to the most threat-prone areas based on the changing variables of the day. Based our analysis, we were able to find out that the Random Forest Classifier optimized over our training data does a fantastic job at saving the Boston Police Department money. There are a number of considerations that one should look into before "blindly" accepting our results -- it may be a an oversimplification to say that the cost of additional police is just the additional hourly wage paid to the officers, and it may also be an optimistic oversimplification to say that if cops know when/where a crime is going to occur, they can prevent it with the probabilities discussed above. However, none the less, we feel that the vast majority of our assumptions were grounded in intensive research and are fair to be considered over a vast range of potential scenarios. Overall, we believe that our projected savings of 67 million dollars over 3 and a half years is significant enough to warrant further investigation into the use of machine learning models in the day to day operations of the Boston Police Department. At the very least, we hope that the explorations shown here encourage others to explore how data science can save money and lives.